Big Data decision support system

Includes bibliographical references.2022 Fall.Each day, the amount of data produced by sensors, social and digital media, and Internet of Things is rapidly increasing. The volume of digital data is expected to be doubled within the next three years. At some point, it might not be financially feasible to store all the data that is received. Hence, if data is not analyzed as it is received, the information collected could be lost forever. Actionable Intelligence is the next level of Big Data analysis where data is being used for decision making. This thesis document describes my scientific contribution to Big Data Actionable Intelligence generations. Chapter 1 consists of my colleagues and I's contribution in Big Data Actionable Intelligence Architecture. The proven architecture has demonstrated to support real-time actionable intelligence generation using disparate data sources (e.g., social media, satellite, newsfeeds). This work has been published in the Journal of Big Data. Chapter 2 shows my original method to perform real-time detection of moving targets using Remote Sensing Big Data. This work has also been published in the Journal of Big Data and it has received an issuance of a U.S. patent. As the Field-of-View (FOV) in remote sensing continues to expand, the number of targets observed by each sensor continues to increase. The ability to track large quantities of targets in real-time poses a significant challenge. Chapter 3 describes my colleague and I's contribution to the multi-target tracking domain. We have demonstrated that we can overcome real-time tracking challenges when there are large number of targets. Our work was published in the Journal of Sensors

Precision Primordial 4^4He Measurement with CMB Experiments

Big bang nucleosynthesis (BBN) and the cosmic microwave background (CMB) are two major pillars of cosmology. Standard BBN accurately predicts the primordial light element abundances ($^4$He, D, $^3$He and $^7$Li), depending on one parameter, the baryon density. Light element observations are used as a baryometers. The CMB anisotropies also contain information about the content of the universe which allows an important consistency check on the Big Bang model. In addition CMB observations now have sufficient accuracy to not only determine the total baryon density, but also resolve its principal constituents, H and $^4$He. We present a global analysis of all recent CMB data, with special emphasis on the concordance with BBN theory and light element observations. We find $\Omega_{B}h^{2}=0.025+0.0019-0.0026$ and $Y_{p}=0.250+0.010-0.014$ (fraction of baryon mass as $^4$He) using CMB data alone, in agreement with $^4$He abundance observations. With this concordance established we show that the inclusion of BBN theory priors significantly reduces the volume of parameter space. In this case, we find $\Omega_{B}h^2=0.0244+0.00137-0.00284$ and $Y_p = 0.2493+0.0006-0.001$. We also find that the inclusion of deuterium abundance observations reduces the $Y_p$ and $\Omega_{B}h^2$ ranges by a factor of $\sim$2. Further light element observations and CMB anisotropy experiments will refine this concordance and sharpen BBN and the CMB as tools for precision cosmology.Comment: 7 pages, 3 color figures made minor changes to bring inline with journal versio

Exploratory Analysis of Pairwise Interactions in Online Social Networks

In the last few decades sociologists were trying to explain human behaviour by analysing social networks, which requires access to data about interpersonal relationships. This represented a big obstacle in this research field until the emergence of online social networks (OSNs), which vastly facilitated the process of collecting such data. Nowadays, by crawling public profiles on OSNs, it is possible to build a social graph where "friends" on OSN become represented as connected nodes. OSN connection does not necessarily indicate a close real-life relationship, but using OSN interaction records may reveal real-life relationship intensities, a topic which inspired a number of recent researches. Still, published research currently lacks an extensive exploratory analysis of OSN interaction records, i.e. a comprehensive overview of users' interaction via different ways of OSN interaction. In this paper we provide such an overview by leveraging results of conducted extensive social experiment which managed to collect records for over 3,200 Facebook users interacting with over 1,400,000 of their friends. Our exploratory analysis focuses on extracting population distributions and correlation parameters for 13 interaction parameters, providing valuable insight in online social network interaction for future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4 on pages 422 to 42

Managing Evidence in Food Safety and Nutrition

Evidence ('data') is at the heart of EFSA's 2020 Strategy and is addressed in three of its operational objectives: (1) adopt an open data approach, (2) improve data interoperability to facilitate data exchange, and (3) migrate towards structured scientific data. As the generation and availability of data have increased exponentially in the last decade, potentially providing a much larger evidence base for risk assessments, it is envisaged that the acquisition and management of evidence to support future food safety risk assessments will be a dominant feature of EFSA's future strategy. During the breakout session on 'Managing evidence' of EFSA's third Scientific Conference 'Science, Food, Society', current challenges and future developments were discussed in evidence management applied to food safety risk assessment, accounting for the increased volume of evidence available as well as the increased IT capabilities to access and analyse it. This paper reports on presentations given and discussions held during the session, which were centred around the following three main topics: (1) (big) data availability and (big) data connection, (2) problem formulation and (3) evidence integration. (C) 2019 European Food Safety Authority. EFSA Journal published by John Wiley and Sons Ltd on behalf of European Food Safety Authority

Big data optimization in electric power systems: a review

There are different definitions of big data, and among them, the most common definition refers to three or five characteristics, called volume, velocity, variety, value, and veracity from (Laney (2001)). Volume could include Tera Byte, Peta Byte, Exa Byte, and Zetta Byte. Velocity describes how fast the data are retrieved and processed ‘‘Batch or streaming”. Variety describes structured, semi-structured, and unstructured data (Laney, 2001, Zikopoulos and Eaton, 2011). Veracity explains the integrity and disorderliness of data, while value refers to how good is the “value” we derive from analyzing data? (Zicari et al., 2016). Electrical power systems are networks of components arrayed to supply, transfer, and use electric power. In power system since models are used to predict and characterize operations. However, there is a necessity for powerful optimization algorithms for information processing to learn models as the size increase of data is becoming a global problem to solve large-scale optimization problems. Any optimization problem includes a real function to be maximized or minimized by systematically determination of input values from an allowed set of values. Richness and quantity of large data sets provide the potential to enhance statistical learning performance but require smart models that use the latent low-dimensional structure for effective 2 data separation. This chapter reviews the most recent scientific articles related to large and big data optimization in power systems. Optimization issues such as logistics in power systems and techniques including nonsmooth, nonconvex, and unconstrained large-scale optimization are presented. After a brief review of big data, scientometric analysis has been applied using keywords of “big data” and “power system.” Besides, keywords analysis, network visualization, journal map, and bibliographic coupling analysis have been done to draw a path on big data works in power system problems. Also, the most common useful techniques in large-scale optimization in power system have been reviewed. At the end of this chapter, metaheuristic techniques in big data optimization are reviewed to show that many efforts have been involved in big data optimization in power system and systematically highlight some perspectives on big data optimization